Data, Information, Knowledge, and Database

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Explain Data, Information, Knowledge, and Database.

Answer: Data, Information, Knowledge, and Database

Data:

Data can be defined as a set of isolated and unrelated raw facts, represented by values, which have little or no meaning, simply because they lack a context for evaluation. Usually, the values are represented in the forms of characters, numbers, or any symbol such as ‘Monica’, ‘35’, and ‘chef’.
Although these words and numbers have some meaning, it is difficult to figure out exactly what
these values signify.

Information:

We can define it as a set of organized and validated collections of data. When the data are processed and converted into a meaningful and useful form, they are known as information. Example: ‘Monica is 35 years old, and she is a chef’.

Knowledge:

It is the act of understanding the context in which the information is used. We can base it on learning through information, experience, and/or intuition. Based on the knowledge, the information can be used in a particular context.

Example: If a hotelier uses the information about Monica (she is a chef) to hire her, he is using his knowledge. Hence, knowledge can also be referred to as a person’s capability and wisdom and how much that person knows about a particular subject. Consequently, it can be said that data constitute information, and information constitutes knowledge.

The figure below shows the relationship between data, information, and knowledge.

Database:

A database can be defined as a collection of related data from which users can efficiently retrieve the desired information. It can be anything from a simple collection of roll numbers, names, addresses, and phone numbers of students to a complex collection of sound, images, and even video or film clippings. Though they are generally computerized, instances of non‐computerized databases from everyday life can be cited in abundance. A dictionary, a phone book, a collection of recipes, and a TV guide are examples of non‐computerized databases. Examples of computerized databases include customer files, employee rosters, books catalog, equipment inventories, and sales transactions.

Difference between Hierarchical, Network and Relational models :

Hierarchical data model

• The relationship between records is of parent-child type.
• The many-to-many relationships cannot be expressed in this model.
• It is a simple, straightforward, and natural method of implementing record relationships.
• This type of model is useful only when there is some hierarchical character in the database.
• In order to represent links among records, pointers are used. Thus relationships among records are physical.
• Searching for a record is very difficult since one can retrieve a child only after going through the record.
• During updating deletion process, the chance of data inconsistency involved.

Network data model

• The relationship between records is expressed in the form of pointers or links.
• The many-to-many relationships can also be implemented.
• Record relationship implementation is quite complex due to the use of pointers.
• Network model is useful for representing such records which have many‐to‐many relationships.
• In network model also the relationship among records are physical.
• Searching a record is easy since there are multiple access paths to a data element.
• No problem of inconsistency exists in network model because a data element is physically located at just one place.

Relational data model

• The relationship between records is represented by a relation that contains a key for each record involved in the relations.
• The many-to-many relationships can be easily implemented.
• Relationship implementation is very easy through the use of a key or composite key field.
• The relational model is useful for representing most of the real-world objects and relationships among them.
• The relational model does not maintain a physical connection among records.
• Data is organized logically as rows and columns and stored in the table.
• A unique, indexed key field is used to search for a data element.
• Data integrity maintaining methods like the Normalization process, etc. are adopted for consistency.